Senior Software Engineer – Machine Learning

Job Description

ESSENTIAL JOB RESPONSIBILITIES :

1. Model Development & Deployment :

– Design, train, and optimize ML models using PyTorch or TensorFlow for production-grade applications.

– Build scalable data pipelines for feature engineering and model training using Pandas, Dask, or equivalent frameworks.

– Implement model evaluation, hyperparameter tuning, and performance monitoring.

2. MLOps :

– Develop and maintain ML workflows using Airflow, Kedro, and MLflow for reproducibility and traceability.

– Automate model deployment and lifecycle management across environments (dev, staging, production).

3. Data Engineering & Processing :

– Handle large-scale datasets efficiently using distributed computing frameworks (Dask, Spark).

– Ensure data quality, consistency, and compliance with governance standards.

– Work on and deploy pipelines to Snowflake / Databricks.

4. Monitoring & Observability :

– Implement model drift detection, performance tracking, and automated retraining strategies.

– Use experiment tracking tools (MLflow, Weights & Biases) for transparency and reproducibility.

5. Collaboration & Documentation :

– Work closely with data scientists, software engineers, and product teams to align ML solutions with business goals.

– Document ML workflows, best practices, and operational guidelines.

REQUIRED QUALIFICATIONS :

– 5-7 years of experience in ML engineering or applied machine learning.

– Strong proficiency in Python and libraries like Pandas, Dask, NumPy, Scikit-learn.

– Hands-on experience with PyTorch or TensorFlow for model development.

– Solid understanding of MLOps tools : Airflow, Kedro, MLflow (or equivalents).

– Experience deploying ML models in production environments (APIs, batch jobs, streaming).

– Hands-on experience with big-data and lakehouse platforms such as Apache Spark, Databricks, and Snowflake.

PREFERRED QUALIFICATIONS :

– Experience with feature stores (Feast, Tecton) and data versioning tools (DVC).

– Experience with Power BI or similar BI tools for analytics and visualization.

– Understanding of model explainability and responsible AI practices.

– Familiarity with containerization (Docker) and orchestration (Kubernetes).

– Exposure to cloud platforms (Azure or AWS) for ML workloads.

– Contributions to open-source ML projects or technical blogs.

Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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